Precision requirements for space-based XCO2 data
C. E. Miller1, D. Crisp1, P. L. DeCola2, S. C. Olsen3, J. T. Randerson4, A. M. Michalak5, A. Alkhaled6, P. Rayner7, D. J. Jacob8, P. Suntharalingam8, D. Jones9, A. S. Denning10, M. E. Nicholls10, S. C. Doney11, S. Pawson12, H. Boesch1, B. J. Connor13, I. Y. Fung14, D. O’Brien10, R. J. Salawitch1, S. P. Sander1, B. Sen1, P. Tans15, G. C. Toon1, P. O. Wennberg16, S. C. Wofsy8, Y. L. Yung16, R. M. Law17
1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California USA2NASA Headquarters, Washington, DC USA3Los Alamos National Laboratory, Los Alamos, New Mexico USA4Department of Earth System Science, University of California, Irvine, California USA5Department of Civil and Environmental Engineering and Department of Atmospheric, Oceanic and Space Sciences, The University of Michigan, Ann Arbor, Michigan USA6Department of Civil and Environmental Engineering, The University of Michigan, Ann Arbor, Michigan USA 7Laboratoire des Sciences du Climat et de l'Environnement/IPSL, CEA-CNRS-UVSQ, Gif-sur-Yvette, France8Division of Engineering and Applied Science and Department of Earth & Planetary Sciences, Harvard University, Cambridge, Massachusetts USA9Department of Physics, University of Toronto, Toronto, Ontario, Canada10Atmospheric Science Department, Colorado State University, Fort Collins, Colorado USA11Woods Hole Oceanographic Institution, Woods Hole, Massachusetts USA12Goddard Earth Science and Technology Center, Baltimore, Maryland, USA and Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland USA.13National Institute of Water and Atmospheric Research, Omakau, Central Otago, New Zealand14Berkeley Atmospheric Sciences Center, University of California, Berkeley, Berkeley, California USA15NOAA Climate Monitoring and Diagnostics Laboratory, Boulder, Colorado USA16Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California USA17DCSIRO Atmospheric Research, Aspendale, Victoria, Australia
Corresponding Author:Charles MillerMS 183-501Jet Propulsion Laboratory4800 Oak Grove DrivePasadena, CA 91109-8099Tel: 818.393.6294Fax: [email protected]
9/10/2023 10:11 PM
123456789
1011121314151617181920212223242526272829303132333435363738394041424344
1
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Abstract
Precision requirements are determined for space-based column-averaged CO2 dry air mole
fraction (XCO2) data. These requirements result from an assessment of spatial and temporal
gradients in XCO2, the relationship between XCO2 precision and surface CO2 flux uncertainties
inferred from inversions of the XCO2 data, and the effects of XCO2 biases on the fidelity of CO2 flux
inversions. Observational system simulation experiments and synthesis inversion modeling
demonstrate that the Orbiting Carbon Observatory (OCO) mission design and sampling strategy
provide the means to achieve these XCO2 data precision requirements.
Sunday, September 10, 2023 10:11 PM 2
23
454647
48
49
50
51
52
53
4
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
1 Introduction
Carbon dioxide (CO2) is a natural component of the Earth’s atmosphere and a strong greenhouse
forcing agent. Concentrations of atmospheric CO2 fluctuated between 185 and 300 parts per
million (ppm) over the last 500,000 years [IPCC, 2001]. However, since the dawn of the
industrial era 150 years ago, human activity (fossil fuel combustion, land use change, etc.) has
driven atmospheric CO2 concentrations from 280 ppm to greater than 375 ppm. Such a dramatic
short-term increase in atmospheric CO2 is unprecedented in the recent geologic record,
prompting Crutzen to label the current era as the Anthropocene [Crutzen, 2002]. Better carbon
cycle monitoring capabilities and insight on the underlying dynamics controlling atmosphere
exchange with the land and ocean reservoirs are needed as society begins to discuss active
management of the global carbon system [Dilling et al., 2003].
Data from the existing network of surface in situ CO2 measurement stations [GLOBALVIEW-
CO2, 2005] indicate that the terrestrial biosphere and oceans have absorbed almost half of the
anthropogenic CO2 emitted during the past 40 years. The nature, geographic distribution and
temporal variability of these CO2 sinks are not adequately understood, precluding accurate
predictions of their responses to future climate change [Friedlingstein et al., 2006; Cox et al.,
2000; Fung et al., 2005]. Inverse modeling of the surface in situ CO2 data [GLOBALVIEW-CO2,
2005] provide compelling evidence for a Northern Hemisphere terrestrial carbon sink, but the
network is too sparse to quantify the distribution of the sink over the North American and
Eurasian biospheres or to estimate fluxes over the Southern Ocean [Gurney et al., 2005; Gurney
et al., 2002; Gurney et al., 2003; Gurney et al., 2004; Law et al., 2003; Baker et al., 2006a].
Sunday, September 10, 2023 10:11 PM 3
56
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
7
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Existing models and measurements also have difficulty explaining why the atmospheric CO2
accumulation varies from 1 to 7 gigatons of carbon (GtC) per year in response to steadily
increasing emission rates.
Space-based remote sensing of atmospheric CO2 has the potential to deliver the data needed to
resolve many of the uncertainties in the spatial and temporal variability of carbon sources and
sinks. Several sensitivity studies have evaluated the improvement in carbon flux inversions that
would be provided by precise, global space-based column CO2 data [Dufour and Breon, 2003;
Houweling et al., 2004; Mao and Kawa, 2004; O'Brien and Rayner, 2002; Rayner et al., 2002;
Rayner and O'Brien, 2001; Baker et al., 2006b]. The consensus of these studies is that satellite
measurements yielding the column-averaged CO2 dry air mole fraction, XCO2, with bias-free
precisions in the range of 1 – 10 ppm (0.3 – 3.0%) will reduce uncertainties in CO 2 sources and
sinks due to uniform and dense global sampling. As Houweling et al. [Houweling et al., 2004]
demonstrated, the precision requirements for space-based XCO2 data vary depending on the spatial
and temporal resolution of the data and the spatiotemporal scale of the surface flux inversion.
Clearly, the highest precision XCO2 data (e.g. 1 ppm or better) would best address the largest
number of carbon cycle science questions. However, this requirement must be balanced against
the significant technical challenges of delivering a satellite measurement/retrieval/validation
system that can produce bias-free, sub-1% precision XCO2 data. The space-based XCO2 data must
also be accurately calibrated to the WMO reference scale for atmospheric CO2 measurements so
that they can be ingested simultaneously with sub-orbital data in synthesis inversion or data
assimilation schemes without producing spurious fluxes.
Sunday, September 10, 2023 10:11 PM 4
89
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
10
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
The Orbiting Carbon Observatory (OCO) was selected by NASA’s Earth System Science
Pathfinder (ESSP) program in July 2002 to deliver space-based XCO2 data products with the
precision, temporal and spatial resolution, and coverage needed to characterize the variability of
CO2 sources and sinks on regional spatial scales and seasonal to interannual time scales [Crisp et
al., 2004]. The mission is designed for a two-year operational period with launch scheduled for
2008, the first year of the Kyoto Protocol commitment period. OCO will join the EOS
Afternoon Constellation (A-Train), flying in a sun-synchronous polar orbit with a constant 1:26
PM local solar time (1326 LST) flyover, a 16-day (233 orbit) repeat cycle and near global
sampling.
The OCO science team analyzed a broad range of measurement and modeling data to define the
science requirements for space-based XCO2 data precision. The products of this investigation
address two fundamental questions:
1. What precision does the OCO XCO2 data product need to improve our understanding of CO2
surface fluxes (sources and sinks) significantly?
2. Does the measurement/retrieval/validation approach adopted in the OCO mission design
provide the needed XCO2 data precision?
This paper analyzes atmospheric CO2 observations and modeling studies of CO2 sources and
sinks to derive the science requirements for space-based XCO2 data precision (Question 1).
Analyses of space-based and sub-orbital measurements, as well as the development and
validation of retrieval algorithms demonstrating the potential of the OCO mission design to
Sunday, September 10, 2023 10:11 PM 5
1112
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
13
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
achieve the required XCO2 precision (Question 2) are the subject of recent [Kuang et al., 2002;
Boesch et al., 2006; Washenfelder et al., 2006] and ongoing studies.
2 XCO2 Precision Requirements
Two community-wide undertakings define the current state of knowledge for the atmospheric
CO2 budget: the GLOBALVIEW-CO2 in situ measurement network [GLOBALVIEW-CO2, 2005]
and the TransCom 3 transport/flux estimation experiment [Gurney et al., 2005; Gurney et al.,
2002; Gurney et al., 2003; Gurney et al., 2004; Law et al., 2003; Baker et al., 2006a]. The
GLOBALVIEW-CO2 network’s emphasis on acquiring accurate measurements through rigorous
experimental methods, constant calibration using procedures and materials traceable to WMO
standards, and continual vigilance against biases has created the recognized reference standard
data set for atmospheric CO2 observations. The network collects surface in situ CO2
measurements at approximately 120 stations worldwide, spanning latitudes from the South Pole
to 82.4°N (Alert, Canada). Typical measurement uncertainties are on the order of 0.1 ppm
(0.03%).
The TransCom 3 project reported estimates of carbon sources and sinks from variations in the
GLOBALVIEW-CO2 data via inverse modeling with multiple atmospheric transport models.
This assessment confirmed that carbon fluxes integrated over latitudinal zones are strongly
constrained by observations in the middle to high latitudes. Flux uncertainties were also
constrained by inadequacies in the transport models and the lack of observations in the tropics.
The latter result is not surprising since the GLOBALVIEW-CO2 network strategy was originally
designed specifically to avoid measurement contamination from air locally influenced by large
Sunday, September 10, 2023 10:11 PM 6
1415
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
16
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
CO2 sources or sinks. The inversions also exhibited significant uncertainties when trying to
distinguish meridional contributions to the fluxes.
Rayner and O’Brien [2001] showed that space-based XCO2 data could dramatically improve our
understanding of CO2 sources and sinks if these measurements provided adequate precision and
spatial coverage. This study used a synthesis inversion model to estimate the surface-atmosphere
CO2 flux uncertainties in 26 continent/ocean basin scale regions. The baseline was established
by using measurements from 56 stations in the ground-based GLOBALVIEW-CO2 network.
The results were compared to simulations that used spatially-resolved, global XCO2 data. Rayner
and O’Brien found that global, space-based XCO2 data with 2.5 ppm precisions (and no biases) on
8o × 10o scales would be needed to match the performance of the existing ground based network
at monthly or annual time-scales. Space-based XCO2 data with 1 ppm precisions were predicted to
reduce inferred CO2 flux uncertainties uncertainties of annual mean fluxes from greater than 1.2
GtC region−1 year−1 to less than 0.5 GtC region−1 year−1 when averaged over the annual cycle.
Additionally, the uncertainties in all regions were more uniform for inversions using the space-
based XCO2 data.
While these simulations clearly illustrate the advantages of precise space-based XCO2 data, they
do not explicitly quantify the data precision required for OCO, because they do not simulate the
spatial and temporal sampling strategy proposed for the OCO mission. Nor do they adequately
characterize the sensitivity of source-sink inversions to XCO2 data precision. To address these
concerns, we combined simulated atmospheric CO2 data and transport models to estimate XCO2
spatial gradients as well as global and regional scale XCO2 variability. A series of Observational
Sunday, September 10, 2023 10:11 PM 7
1718
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
19
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
System Simulation Experiments (OSSE’s) sampled the synthetic CO2 fields using strategies
simulating the GLOBALVIEW-CO2 surface network [GLOBALVIEW-CO2, 2005] as well as the
OCO satellite. Inverse modeling of these data characterized the relationship between the inferred
surface flux uncertainties and uncertainties in the space-based XCO2 data. We also investigated the
use of inversions to detect bias in the XCO2 data and what level of sensitivity such analyses would
provide.
2.1 XCO2 Spatial and Temporal Gradients
Distributions of atmospheric CO2 were simulated with the Model of Atmospheric Transport and
Chemistry (MATCH) three-dimensional atmospheric transport model [Olsen and Randerson,
2004]. MATCH represents advective transport using a combination of horizontal and vertical
winds and has parameterizations of wet and dry convection and boundary layer turbulent mixing
[Rasch et al., 1994]. MATCH operates off-line using archived meteorological fields which for
this study were derived from the NCAR Community Climate Model version 3 with T21
horizontal resolution (approximately 5.5° × 5.5°) and 26 vertical levels from the surface up to 0.2
hPa (about 60 km) on hybrid sigma pressure levels. The top of the first model level is
approximately 110 m. The meteorological fields represent a climatologically ‘‘average’’ year
rather than any specific year. This meteorological data was archived every 3 model hours and
was interpolated to the 30-min MATCH time step. In this configuration MATCH has an
interhemispheric transport time of approximately 0.74 years, about in the middle of the 0.55 year
to 1.05 year range of the models that participated in the TransCom 2 experiment [Denning et al.,
1999]. A single year of dynamical inputs was recycled for the multiyear runs used in this study.
Sunday, September 10, 2023 10:11 PM 8
2021
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
22
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Constraints on CO2 sources and sinks incorporated fossil fuel emissions as estimated by Andres
et al. [1996], atmosphere-oceanic exchange as estimated from sea-surface pCO2 measurement
by Takahashi et al. [2002], and biospheric fluxes modeled using the Carnegie-Ames-Stanford
Approach (CASA) model [Randerson et al., 1997], including a diurnal cycle of photosynthesis
and respiration. Simulated XCO2 data were obtained from the model output by integrating
vertically according to the OCO averaging kernel at the T21 horizontal resolution. In these
simulations terrestrial ecosystem exchange was annually balanced; in other words, we omitted a
‘‘missing’’ carbon sink necessary to balance fossil carbon sources with the atmospheric CO2
growth rate [Gurney et al., 2002; Tans et al., 1990].
The 15th of each month was taken as a sample representative day for each month. Data were
extracted for 1300 local time globally, as a preliminary approximation of XCO2 as would be
observed by OCO. The resulting data are presented for January and July 2000 in Figure 1. These
data differ slightly from the monthly mean XCO2 maps presented in Figure 9 of Olsen and
Randerson [2004], capturing more of the instantaneous XCO2 seasonal variability. All model
values are reported relative to the annual mean surface mixing ratio south of 60° South. Figure 1
shows that the Northern Hemisphere XCO2 variability is typically about 6 ppm. This is only 50%
of the amplitude of the seasonal cycle in the near-surface concentrations of CO2 (see Figure 5 of
Olsen and Randerson [2004]). Somewhat larger high frequency variations, associated with
passing weather systems and strong source regions, are also common. The monthly peak-to-peak
amplitude of Southern Hemisphere XCO2 is typically 2-3 ppm while the annual peak-to-peak
amplitude varies from 6-7 ppm over tropical forests to less than 3 ppm over the Southern Ocean.
Sunday, September 10, 2023 10:11 PM 9
2324
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
25
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
These results indicate that space-based XCO2 data with precisions better than 2 ppm are needed to
resolve the peak-to-peak amplitudes in monthly and annual XCO2. This precision is also sufficient
to resolve regional scale meridional variations over the northern hemisphere boreal forests or the
Southern Ocean. The OCO sampling strategy (Section 4) is specifically designed to return
space-based measurements with the high sensitivity and dense sampling in space and time
required to attain this precision even when cloud and aerosol interference prevents observations
of the complete atmospheric column in the majority of the observed scenes.
2.2 Global and Regional XCO2 Variability
Global XCO2 spatial variability was quantified by analyzing the CASA/MATCH XCO2 data
calculated for 2000. Raw and experimental variograms were calculated to quantify the global
variability of XCO2. The raw variogram is defined for any two measurements as [Cressie, 1991]
(1)
where (h) is the raw variogram, z(x) is a measurement value at location x, z(x′) is a
measurement value at location x′, and h is the separation distance between x and x’. The distance
was calculated using the great circle distance between points on the surface of the earth (e.g.
Michalak et al. 2004):
(2)
where the coordinates are the latitude and longitude, respectively, of the sample
locations, and r is the mean radius of the earth. The raw variogram values are averaged for
different ranges of separation distance to obtain the experimental variogram. Because the
variogram is designed to represent the portion of the XCO2 distribution that cannot be represented
Sunday, September 10, 2023 10:11 PM 10
2627
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
28
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
by a deterministic trend, the consistent North-South XCO2 gradient was accounted for by
detrending the data with respect to latitude, using the simulated latitudinal gradient for 1300 LST
each month. In this way, the variograms represent the stochastic, spatially-correlated, portion of
the XCO2 distribution, which is the portion that will need to be estimated to obtain a continuous
XCO2 distribution based on the point measurements taken by OCO. The resulting variograms are
presented in Figure 2.
The experimental variogram for each month was fitted using an exponential theoretical
variogram model [Cressie, 1991; Michalak et al., 2004]
(3)
Where 2 is the semivariance and L is the length parameter. The theoretical variogram describes
the decay in spatial correlation between pairs of XCO2 measurements as a function of physical
separation distance between these samples. The overall variance at large separation distances is
22 and the practical correlation range is approximately 3L. The 2 and L parameters were
estimated using a least-squares fit to the raw variogram. The fitted variograms are presented in
Figure 2, and the global variance and correlation range for each month are summarized in Table
1. The correlation length represents the distance at which the expected covariance between z(x)
and z(x′) approaches zero, and the measurement z(x′) no longer provides useful information about
the XCO2 value z(x). The variance indicates the maximum uncertainty at unsampled locations, in
the absence of nearby measurements, assuming the overall mean or trend is known.
This analysis shows that the CASA/MATCH XCO2 field exhibits significant spatial correlation
and that the degree of spatial correlation varies throughout the year. The variance is higher
Sunday, September 10, 2023 10:11 PM 11
2930
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
31
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
during the Northern Hemisphere summer and lower in winter. The seasonality of the global
correlation length is less pronounced, but follows a similar pattern to that of the variance. These
two factors have opposite effects on the required sampling intensity (i.e. a higher variance leads
to a larger number of required sampling locations whereas a longer correlation length reduces the
number of required samples). Overall, because of the stronger seasonality of the variance of the
XCO2 distribution, it is expected that a larger fraction of samples will need to be processed in
summer months in order to achieve a specified level of uncertainty in the interpolated XCO2 field.
Based on this global analysis, an average sampling interval of approximately 1500 km will be
required globally to achieve an interpolated XCO2 uncertainty with a standard deviation below 1
ppm. This represents the average sampling interval over the entire year, although individual
months will require either sparser or more dense measurements depending on the statistical
characteristics of the XCO2 variability in each month. This estimate is based on:
(4)
where L and 2 are taken from the theoretical variogram and Vmax is the maximum allowable
uncertainty, expressed as a variance. In the above calculation, we used the annual mean
parameters and Vmax = 1 ppm2. Note that this analysis assumes that either (i) the value sampled
by OCO is representative of the average XCO2 on the scale modeled by CASA/MATCH, or that
(ii) the covariance structure as estimated at the CASA/MATCH model grid scale is valid at the
smaller OCO measurement scale. In reality, OCO will measure at a significantly smaller scale
than the CASA/MATCH model resolution, and data at smaller scales tend to exhibit more
variability relative to measurements that represent averages at larger scales. For these reasons,
we expect the sampling interval required to achieve a maximum Vmax = 1 ppm2 uncertainty in the
Sunday, September 10, 2023 10:11 PM 12
3233
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
34
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
interpolated XCO2 product to be smaller for the OCO sampling scale relative to the
CASA/MATCH data. Note also that we have not considered measurement errors in this
calculation, which would again increase the amount of sampling required to constrain the
interpolated error to a given uncertainty threshold.
To assess the regional variability of XCO2, a separate variogram was constructed for each grid cell
of the 5.5o × 5.5o model output (2048 cells globally), centered at that grid cell. In calculating the
raw variogram for each grid cell, only pairs of data points with at least one member within a
2000 km radius of the grid cell were considered. Therefore, the raw variogram consisted of data
pairs where either (i) both measurements were within 2000 km of the central grid cell, or (ii) one
measurement was within 2000 km of the central grid cell and the other was not. In essence, this
approach quantifies the variability between measurements in the vicinity of a grid cell and the
global distribution.
The regional-scale raw variograms were fitted using weighted least-squares and an exponential
variogram, giving greater weight to pairs of points at shorter separation distances and
constraining the correlation length to less than 20,000km. The resulting correlation lengths and
variances of the fitted theoretical variograms are presented in Figures 3 and 4, respectively.
These global maps of parameters describe the regional correlation structure of the
CASA/MATCH XCO2. The correlation structure exhibits temporal variability, as was seen in
Figure 2, as well as strong spatial variability. This can be observed both in the variance and
correlation lengths of the XCO2 distributions. For example, XCO2 in the northern hemisphere is
correlated over shorter distances than the global average (Figure 3). The variability exhibited by
Sunday, September 10, 2023 10:11 PM 13
3536
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
37
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
the XCO2 distribution is caused both by the scales and degree of variability of the underlying
fluxes, as well as the variability induced by atmospheric transport.
The results of the regional XCO2 variability analysis are qualitatively consistent with the results of
Lin et al. [2004], who also found longer correlation lengths over the Pacific relative to
continental North America in their analysis of aircraft-derived partial-XCO2 fields. A quantitative
comparison is difficult to establish because Lin et al. [2004] used a non-stationary power
variogram to represent CO2 variability. Such a variogram does not have a finite maximum
variance (i.e. sill) or correlation length to compare to those presented in Figures 3 and 4. One
quantitative comparison that can be made is a calculation of the separation distance at which the
expected difference in XCO2 at two sampling locations is expected to reach a specified variance.
Based on the variogram used in Lin et al. [2004], the separation distance at which the squared
difference between vertically-integrated CO2 concentrations (< 9km) is expected to reach 1 ppm2
is 57 km over the North American continent in June 2003, and 727 km over the Pacific Ocean.
Data over the Pacific Ocean were a composite of multiple years of springtime (February to
April) and fall (August to October) data. For the CASA/MATCH data, the separation distance is
460 km over the North American continent for June 2000, and 13,600 km (March 2000) and
5200 km (September 2000) over the Pacific Ocean. The two sets of results are consistent in
showing greater spatial variability over the continental regions, but Lin et al. [2004] shows more
overall variability at smaller scales. The higher variability inferred by Lin et al. [2004] is most
likely largely due to the scale at which the aircraft campaign measurements were taken relative
to the scale of the CASA/MATCH modeled data, and the limited vertical extent of the aircraft
profiles. As was previously discussed, data at finer scales typically exhibit more variability
Sunday, September 10, 2023 10:11 PM 14
3839
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
40
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
relative to coarser data. This will need to be considered further in interpreting global model data
in the context of fine scale OCO measurements. Regional scale XCO2 variability will also be
driven by local conditions and meteorology [Nicholls et al., 2004]. Spatial and temporal
heterogeneity of the XCO2 covariance structure must therefore be taken into account in the design
of a sampling strategy and retrievals.
3 CO2 Fluxes from XCO2 Inversions
3.1 Relationship between OCO XCO2 Precision and Surface CO2 Flux Uncertainties
The synthesis inversion methods of Rayner and O’Brien [O'Brien and Rayner, 2002; Rayner et
al., 2002; Rayner and O'Brien, 2001] were used to evaluate the impact of particular OCO
mission design choices and the resultant regional scale XCO2 data precisions on the surface CO2
flux uncertainties. That study used a higher resolution than Rayner and O’Brien [2001] (116
source regions vs. 26) and used an orbit simulator to sample the model in accordance with
satellite orbit and viewing geometry. This is more stringent than the uniform monthly mean
sampling assumed by Rayner and O’Brien [2001]. The new study still retrieves monthly mean
fluxes. We note that Chevallier et al. [2006] has increased both spatial and temporal resolution
of the retrieved sources and still shows considerable potential for OCO measurements.
Rayner et al. [2002] also studied the ability to retrieve actual fluxes from a set of synthetic or
pseudodata sampled to mimic various in-situ or remotely sensed products. They used fluxes
representing fossil fuel combustion, the ocean air-sea gas exchange and the seasonal flux from
the terrestrial biosphere. We follow that setup here.
Sunday, September 10, 2023 10:11 PM 15
4142
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
43
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Following Rayner and O’Brien [2001], a baseline for comparisons with the simulated space-
based XCO2 data was established by performing synthesis inversion experiments to estimate the
CO2 flux uncertainties for the GLOBALVIEW-CO2 surface CO2 monitoring network over the
seasonal cycle. These flux errors are expressed in grams of carbon per square meter per year,
(gC m2 yr1). The prior uncertainty for all regions was assumed to be 2000 gC m2 yr1 for
monthly fluxes. This is a very weak prior estimate so as not to artificially inflate the
performance of the inversion system. Monthly mean CO2 flask data were simulated with an
uncertainty that assumed that the monthly mean had been constructed from 4 samples (i.e. one
per week) from each of the 72 surface stations. Results are shown for January (Jan) and July (Jul)
in Figure 5a-b. For this baseline case most regions have flux uncertainties in excess of 1000 gC
m2 yr1 with uncertainties greater than 1500 gC m2 yr1 typical for most land regions.
Figures 5c-d show the flux uncertainties inferred by inverting a simulated network containing 25
continuous CO2 monitoring sites plus 47 sites reporting weekly CO2 flask measurements. The
continuous monitoring sites were located based on current in situ measurements. Continuous
surface measurements produce their greatest benefits in the vicinity of measurement stations that
are located well away from strong sources and sinks. Even with the addition of the continuous
CO2 measurements, flux uncertainties remain greater than 1000 gC m2 yr1 in most continental
regions near strong surface fluxes because of the limited spatial coverage offered by the
continuous monitoring stations. The largest uncertainties are seen in South America, central
Africa, and southern Asia.
Sunday, September 10, 2023 10:11 PM 16
4445
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
46
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Figure 6 shows January (Jan) and July (Jul) CO2 flux uncertainties from synthesis inversion
simulations assuming XCO2 data sampled along the OCO orbit track with 1ppm (0.3%, Figure 6a-
b) and 5 ppm (1.5%, Figure 6c-d) precisions for monthly averages on 4o latitude 5o longitude
scales. For well-constrained regions in which the prior estimate has little impact, the flux
uncertainty is proportional to the data uncertainty. The relationship breaks down for small
regions (such as the subdivision of Australia in Figs 5 and 6) and at high latitudes where the
measurement frequency is lower. Such problems can be reduced by calculating fluxes over larger
spatial scales after performing the inversion.
The Northern Hemisphere terrestrial carbon sink is thought to absorb about 1 GtC yr1 from the
atmosphere [Gurney et al., 2002]. The results presented in Figure 6 indicate that space-based
XCO2 data with monthly averaged precision of 1 – 2 ppm (0.3 to 0.5%) will yield flux
uncertainties no greater than ~100 gC m2 yr1 or 0.1 GtC (106 km2)1 yr1 (with the exception of
Greenland). Inversions using such space-based XCO2 data should be able to detect the 1 GtC yr1
carbon sink if it is confined to an area or areas smaller than a few 1000 km × 1000 km regions,
e.g. Northeastern North America. We anticipate even greater sensitivity to detecting such a sink
if space-based XCO2 and in situ surface CO2 data are combined in the inversion.
Comparisons of the flux uncertainties inferred from the 5 ppm precision XCO2 data (Figure 6c-d)
with results from the baseline inversion (Figure 5 a-b) show that, even at this degraded precision,
the satellite data still provide a better constraint on surface fluxes for most regions. Augmenting
the surface network with continuous monitoring stations (Figure 5c-d) improves the surface flux
constraints for regions around one of these continuous monitoring sites, but still provides
Sunday, September 10, 2023 10:11 PM 17
4748
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
49
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
inadequate constraints in other regions, particularly tropical forests. Because of uniform spatial
sampling over land and ocean and sheer data volume, space-based XCO2 data will make a
substantial impact on reducing continental scale flux uncertainties even at 5 ppm precision.
3.2 Effects of Systematic XCO2 Bias on CO2 Flux Inversions
The precision requirements space-based XCO2 data presented in Section 2 assumed random errors
with no significant spatially- or temporally-coherent biases. This section addresses the potential
impact of systematic biases in the space-based XCO2 data on inferred CO2 flux uncertainties.
Systematic biases might result from such measurement considerations as signal to noise ratio,
viewing geometry, whether the observations were made over land or ocean, spatial variations in
clouds or aerosols, topographic variations, diurnal effects on the vertical CO2 profile, etc. The
effects of such biases on flux uncertainties depend on their spatial and temporal scale since CO2
sources and sinks are inferred from XCO2 gradients. Constant global biases do not compromise
XCO2-only flux inversions because they introduce no spurious gradients in the XCO2 fields that
could be misinterpreted as sources or sinks; however, a constant bias in space-based XCO2 data
would complicate inversions or assimilations that also included suborbital data or data from
other satellite platforms. Biases occurring on spatial scales smaller than ~30 km are not a major
concern because they will be indistinguishable from random noise contributions like scene-
dependent XCO2 variability. Coherent biases on 100 – 5000 km horizontal scales pose the greatest
threat to the integrity of space-based XCO2 data and must be corrected below detectable levels.
Temporal biases occurring on seasonal time-scales will also complicate CO2 flux inversion and
assimilation studies.
Sunday, September 10, 2023 10:11 PM 18
5051
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
52
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Different biases were considered to define requirements for the OCO calibration and validation
programs. For example, CASA-MATCH simulations indicate that a 1 GtC yr1 northern
hemisphere carbon sink superimposed on a background emissions source of 6 GtC yr 1 coming
from the northern hemisphere would create an additional 0.4 ppm XCO2 gradient between 45N
and 45S (ie. it would contribute about 1/6 of the XCO2 gradient shown in Figure 1). If the space-
based XCO2 data were systematically biased by +0.2 ppm in the Northern Hemisphere and −0.2
ppm in the Southern Hemisphere, inversion modeling would fail to detect the sink. If they were
systematically biased by −0.2 ppm in the Northern Hemisphere and +0.2 ppm in the Southern
Hemisphere, inversion modeling would infer a Northern Hemisphere sink of 2 GtC yr 1 rather
than 1 GtC yr1. In either of these hypothetical cases, the large discrepancy between the inferred
fluxes and prior estimates would signal potential problems.
The model described in Sec. 3.1 was used to assess the impact of a small, spatially coherent land-
ocean XCO2 bias on surface flux inversions, and to determine whether such a bias could be
detected. We performed two separate forward simulations. The control case was derived
assuming a 1400 LST orbit while test case used XCO2 data biased by +0.1ppm over land. The two
resulting flux fields were input to the CRC-MATCH transport models and subsampled at four-
hourly intervals at 72 stations. The (bias control) near-surface CO2 concentration differences
for the annual mean are shown in Fig. 7. One might expect XCO2 biases to reflect much larger
errors near the surface, since spatial variations in CO2 (and many sources of bias) are largest
there. In this test, the near-surface CO2 concentration differences are generally less than ±0.2
ppm. However, the results are spatially coherent with positive differences inferred over land and
negative differences inferred over the oceans. Thus, the comparison of surface CO2
concentration data and XCO2 data flux inversions clearly reveals a land-ocean bias in the XCO2
Sunday, September 10, 2023 10:11 PM 19
5354
418
419
420
421
422
423
424
425
426
427
428
429430
431
432
433
434
435
436
437
438
439
440
441
55
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
retrievals, even when the bias is only 0.1 ppm.
My understanding is that the differences (actually time series of differences at 4 hourly
frequency) were inverted using the surface network. Then the fluxes produced from this
inversion were added to the fluxes from the test (biased) inversion. In Fig 8 we have the
difference between this set of corrected fluxes (biased fluxes from column + corrections from
surface) and the original flux estimates from the unbiased columns. Peter – if this agrees with
your memory/notes of what we did then I suggest you have a look at both paragraphs (L411-433)
and re-write if necessary because at the moment I don't think it describes what we did.
We corrected the artificially biased XCO2 retrievals by inverting the time series of (bias control)
differences simulated at each of the 72 surface sites with a 4-hour sampling frequency. The
fluxes produced from this inversion were added to the fluxes from the test (biased) inversion. Fig
8 depicts the difference between these corrected fluxes and the original flux estimates from the
unbiased XCO2 data. Most regions show differences smaller than 10 gC m−2 yr−1. More
importantly, the differences are no longer spatially coherent. Larger differences occur for land
regions in the tropics where the surface network (black circles shown in Fig. 7) is sparse. The
annual mean land-ocean partition is corrected by the inversion of the surface data (shifting 1.6
GtC yr-1 from land to ocean).
These tests increase our confidence in the ability to validate space-based XCO2 data to a precision
of 1 ppm because, while biases on the order of 0.1 ppm will be difficult to detect directly, they
can be detected and corrected by combining the space-based measurements with observations
Sunday, September 10, 2023 10:11 PM 20
5657
442
443444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
58
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
from a reasonable number of surface stations. The 72 stations used here are more than currently
monitor CO2 continuously but for both practical and scientific reasons (e.g. Law et al. [2002])
there is a trend towards more continuous monitoring. We note that the procedure employed here
is demanding on transport models. These tests also show the importance of CO2 sources-sink
inversions (Level 4 data products) in validating the XCO2 retrievals (Level 2 data products).
3.3 CO2 Flux Constraints on Regional and National Spatial Scales
Another OSSE was performed to assess the precision requirement for XCO2 data to constrain CO2
fluxes on regional and national scales. We examined CO2 fluxes in Asia in spring. Studies by
Suntharalingham et al. [2004] and Palmer et al. [2006] previously showed that high-density
aircraft observations from the March-April 2001 TRACE-P aircraft campaign in Asian outflow
over the NW Pacific [Jacob et al., 2003] provide valuable constraints on the CO2 flux from
different countries in Asia. We evaluate here the extent to which OCO-like XCO2 data can
disaggregate the CO2 fluxes from China, India, Japan, Korea, and Southeast Asia (Figure 9).
Pseudo-observations for March – April 2001 were generated using the GEOS-CHEM global
three-dimensional chemistry transport model. Details of the GEOS-CHEM model and the CO2
simulation may be found in Suntharalingam et al. [2004]. We used version 4.21 of GEOS-
CHEM driven by GEOS-3 assimilated meteorological fields for 2000 and 2001, at a horizontal
resolution of 2º latitude 2.5º longitude. GEOS-CHEM was also employed as the forward
model in the inversion analysis. The seasonal CO2 surface flux in the model, aggregated over the
regions considered here, is listed in Table 2. These flux estimates are based on the source
inventories used by Suntharalingam et al. [2004]. These fluxes were adopted as the true surface
Sunday, September 10, 2023 10:11 PM 21
5960
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
61
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
fluxes of CO2 in our inversion analysis. The inversion was conducted using an a priori estimate
of the fluxes obtained by perturbing the “true” fluxes within the a priori errors.
The GEOS-CHEM simulation was conducted from 1 January 2000 to April 30 2001, starting
from observed CO2 latitudinal gradients. The first 13 months (to January 31 2001) were used for
initialization of the model CO2 background. Starting on February 1 2001, CO2 surface fluxes for
the different regions of Figure 9 were transported as separate tracers in the model; the
background concentration as of January 31 was carried forward as an additional tracer with no
further sources and sinks. We generated retrievals for OCO in March-April 2001 by sampling
this pseudo-atmosphere along the satellite orbit, transforming the modeled profiles with the OCO
column averaging kernel, and adding noise. The retrieved pseudo-data were limited to the region
between the equator and 64ºN and from 2.5º to 167.5ºE for the purposes of this test. Model
output was provided with 3-hour temporal resolution and we used the modeled CO2 profile
closest to the 1326 LST OCO sampling time. Contributions from the different model tracers to
the simulated CO2 concentrations were used to construct the Jacobian of the forward model for
purpose of the inversion.
To assess the precision requirements for XCO2 in terms of monthly mean data with 4ox5o
resolution, the pseudo-retrievals were correspondingly averaged and the results for March 2001
are shown in Figure 10. In generating these observations we neglected the loss of data from
cloud cover. Such data loss is inconsequential because of the large number of observations, as
long as there are no correlations between CO2 column and cloud cover [Rayner et al., 2002].
Sunday, September 10, 2023 10:11 PM 22
6263
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
64
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
We assumed that the CO2 flux errors from the different regions in Table 2 were uncorrelated,
with a uniform a priori uncertainty of 50%. The actual uncertainties will vary with the relative
contributions of different sectors to the regional CO2 sources. Emissions from fossil fuel use in
the industrial and vehicular sectors are known to within about 10% [Streets et al., 2003].
Emissions from the domestic fuel use sector (residential coal and biofuels), a major source in
east Asia, may have uncertainties of about 50% on national scales [Palmer et al., 2003;
Suntharalingam et al., 2004]. Emissions from biomass burning are uncertain by at least a factor
of 2 [Palmer et al., 2003]. Net fluxes from the terrestrial biosphere in East Asia are uncertain by
~100% [Gurney et al., 2002]. We also assumed no error covariance between individual OCO
observations.
Modeled XCO2 values for March 2001, shown in Fig. 10a, were convolved with 0.3% Gaussian
measurement noise to generate the pseudo-observations. The a posteriori CO2 surface fluxes
determined from the inversion are compared with the a priori and true fluxes in Fig. 10b. We
aggregated the fluxes from Japan and Korea because the 4º x 5º monthly mean XCO2 pseudo-data
prohibited discriminating between emissions from these regions. The inverse model accurately
updated the CO2 fluxes for the resulting 4 Asian regions. The flux uncertainty was significantly
reduced for all regions, with the exception of the combined Japanese and Korean region (JPKR).
The a posteriori errors for China, India, and south-east Asia improved to 16%, 20%, and 27%,
respectively, from the a priori uncertainty of 50%.
The result of the inversion analysis depends strongly on the observation error and on the a priori
uncertainties assumed for the regional CO2 sources. Figure 11 shows the relative uncertainties of
Sunday, September 10, 2023 10:11 PM 23
6566
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
67
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
the a posteriori sources as a function of the observation error for three values of the a priori
source uncertainty (50%, 100%, and 150%). The observations constrain the inferred CO2 fluxes
from the Asian regions, with the exception of JPKR, when XCO2 errors are 0.3% or less. With
larger observation errors, it becomes more difficult for the inversion to resolve the contributions
from individual regions, and the curves associated with the different a priori assumptions
diverge. For example, with an observation uncertainty of 0.6% the a posteriori estimate for India
is sensitive to the assumed a priori error and the estimates for both the Indian and Southeast
Asian regions become strongly correlated with those from China (not shown). As expected, the
error estimates for JPKR are most sensitive to the assumed a priori error, since this is the least
well constrained region in the inversion. These results demonstrate the potential for OCO
observations to accurately disaggregate CO2 surface fluxes from India and China. This is
important since these two regions are rapidly industrializing and experiencing significant land
use changes.
4 The OCO Sampling Approach
The modeling studies of spatial and temporal XCO2 variability and surface CO2 flux inversions
define the science measurement requirements for space-based XCO2 data. The OCO sampling
strategy is designed to return observations that maximize precision and minimize bias in the
space-based XCO2 data so as to obtain the most accurate possible constraints on regional scale
surface CO2 fluxes [Crisp et al., 2004]. In situ measurements from tower [Bakwin et al., 1998;
Haszpra et al., 2005] and aircraft [Anderson et al., 1996; Andrews et al., 1999; Andrews et al.,
2001a; Andrews et al., 2001b; Bakwin et al., 2003; Machida et al., 2003; Matsueda et al., 2002;
Ramonet et al., 2002; Sawa et al., 2004; Vay et al., 2003] have shown that vertical
Sunday, September 10, 2023 10:11 PM 24
6869
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
70
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
concentrations of CO2 can vary significantly, especially in the boundary layer. Therefore, space-
based measurements that sample the full atmospheric column are required. Space-based XCO2 data
must also capture variations in XCO2 on seasonal to interannual time scales globally without
diurnal biases. Measurements made from a polar, sun-synchronous orbit address these
requirements. Scattering of solar radiation by clouds and optically thick aerosols prevents
measurements that sample all the way to the surface. Spatial inhomogeneities within individual
soundings (variations in topography, surface albedo, etc.) can compromise the accuracy of XCO2
retrievals. A small sampling footprint mitigates both of these issues. The space-based XCO2 data
must be precise and unbiased over land and ocean (Sec. 3.2), despite low surface albedos or
other effects that may limit signal-to-noise levels. OCO includes both nadir and glint observing
modes to mitigate concerns about signal-to-noise issues and a point-and-stare (target) mode for
routine validation of XCO2 retrievals over a range of latitudes, viewing angles, and geophysical
conditions. We also address whether space-based XCO2 data acquired via the OCO sampling
strategy is representative of the regional scale XCO2 fields it samples.
4.1 Space-based sampling strategy
The Observatory will fly at the head of the Earth Observing System (EOS) Afternoon
Constellation (A-Train), a polar, sun-synchronous orbit that follows the World Reference System
2 (WRS-2) ground track, providing global sampling with a 16-day repeat cycle and 1326 LST
observations. This local time of day is ideal for spectroscopic observations of CO2 in reflected
sunlight because the sun is high, maximizing the measurement signal to noise ratio, and because
XCO2 is near its diurnally-averaged value at this time of day. This orbit also facilitates direct
comparisons of OCO observations with complementary data products from Aqua (e.g. AIRS
temperature, humidity, and CO2 retrievals; MODIS, Cloudsat and CALIPSO clouds and
Sunday, September 10, 2023 10:11 PM 25
7172
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
73
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
aerosols; MODIS surface type), Aura (TES CH4 and CO), and other A-Train instruments. The
16-day repeat cycle enables tracking global XCO2 variations twice per month, with nearby revisits
(~100 km horizontal separation) occurring at least once every six days.
Each OCO sounding includes bore-sighted spectra of solar radiation reflected from the Earth’s
surface in the 0.76 m O2 A-band and the CO2 bands at 1.61 and 2.06 m. XCO2 is retrieved from
the CO2/O2 ratio. The OCO instrument and observing strategy were designed to obtain a
sufficient number of useful soundings to characterize the XCO2 distribution accurately on regional
scales, even in the presence of patchy clouds. The OCO instrument records up to 8 soundings
along a 10 km wide (nadir) cross-track swath at 3.0 Hz, yielding up to 24 soundings per second.
As the spacecraft moves along its ground track at 6.78 km/sec, each sounding will have a surface
footprint with dimensions of 1.25 km 2.26 km at nadir, yielding up to 350 soundings over
each 1o latitude increment along the orbit track.
OCO will collect science observations in Nadir, Glint, and Target modes. The same sampling
rate is used in all three modes. In Nadir mode, the spacecraft points the instrument boresight to
the local nadir, so that data can be collected along the ground track directly below the spacecraft.
Science observations will be collected at all latitudes where the solar zenith angle is less than 85.
This mode provides the highest spatial resolution on the surface and is expected to return more
useable soundings in regions that are partially cloudy or have significant surface topography.
Glint mode was designed to provide superior signal-to-noise (SNR) performance at high latitudes
and over dark ocean where Nadir mode observations might have difficulty meeting the XCO2
Sunday, September 10, 2023 10:11 PM 26
7475
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
76
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
precision requirements. In Glint mode the spacecraft points the instrument boresight toward the
bright “glint” spot, where solar radiation is specularly reflected from the surface. Glint
measurements will provide 10 – 100 times higher SNR over the ocean than Nadir measurements
[Kleidman et al., 2000; Cox and Munk, 1954]. Glint soundings will be collected at all latitudes
where the local solar zenith angle is less than 75o. The nominal OCO mission operations plan is
to switch between Nadir and Glint modes on alternate 16-day repeat cycles such that the entire
Earth is sampled in each mode on monthly time scales. Operating in both Nadir and Glint modes
each month is an ideal way to detect global bias in the XCO2 product since the retrieved XCO2 data
and inferred carbon fluxes should be independent of the observation technique.
Target mode will acquire “point and stare” validation observations of specific stationary surface
targets as the observatory flies overhead. Simultaneous acquisition of solar-viewing Fourier
transform spectrometer (FTS) data from a Target-ed OCO validation site provides a means to
transfer calibration of the space-based XCO2 data to the WMO scale for atmospheric CO2
[Washenfelder et al., 2006; Boesch et al., 2006]. Target passes will last up to 8 minutes,
providing up to 10,000 soundings over a given site at local zenith angles between 0 and 85.
Target mode enables the OCO Team to assess the impact of viewing geometry on XCO2 retrievals.
Furthermore, the FTS validation sites have been distributed from pole-to-pole to identify and
remove any biases that might arise as a function of latitude or region. Target passes will be
conducted over each of the OCO validation sites 1 – 2 times per month. The Observatory will
also regularly acquire Target data over homogeneous Earth scenes such as the Sahara desert
[Cosnefroy et al., 1996; Dinguirard and Slater, 1999] and Railroad Valley, CA [Abdou et al.,
2002] for vicarious radiometric calibration.
Sunday, September 10, 2023 10:11 PM 27
7778
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
79
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
The OCO observing strategy provides thousands of samples on regional scales for each 16-day
orbit track repeat cycle. The observatory will collect up to 3400 soundings every time it flies
over each 1000 km 1000 km region. There are at least five overflights of each region every 16
days, resulting in up to 17,000 regional soundings per repeat cycle. Analysis of high spatial
resolution MODIS cloud data aggregated to the 3 km2 size of the OCO footprint indicates that on
average only about 24% of these soundings will be sufficiently clear for accurate XCO2 retrieval.
Breon et al. [2005] recently analyzed GLAS data and determined that the global fraction of clear
sky scenes ( < 0.01) is ~15% with an additional ~20% of scenes having total cloud and aerosol
optical depth < 0.2, the approximate threshold for which precise XCO2 retrievals are possible
[Crisp et al., 2004; Kuang et al., 2002]. Thus, the OCO sampling strategy should yield between
2600 and 6000 soundings per region per 16-day repeat cycle as candidates for XCO2 retrievals.
Multiple passes through each region also provide constraints on sub-regional spatial and
temporal XCO2 variations that are associated with local topography, passing weather systems or
other phenomena, and provide the data needed to identify systematic biases that could
compromise the data, even in persistently cloudy regions. The large number of mostly clear
scenes provides sufficient sampling statistics to support the OCO baseline plan of alternating
between Nadir and Glint observations on alternating 16-day repeat cycles.
If all errors in the space-based XCO2 retrievals were purely random and individual soundings had
uncertainties of 16 ppm, then one would need to perform retrievals on only 256 out of the 2600 –
6000 candidate soundings to achieve XCO2 estimates with precisions of 1 ppm on 16 day intervals.
The OCO team has adopted a more stringent 6 ppm worst case single sounding XCO2 data
Sunday, September 10, 2023 10:11 PM 28
8081
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
82
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
precision requirement to ensure that useful data can be collected even in persistently cloudy
regions (ie. the Pacific Northwest coast of North America or Northern Europe in the winter),
where typical data yields are anticipated to be much less than 10% of the total number of
soundings. Sensitivity analyses indicate that a 6 ppm single sounding precision requirement also
provides adequate precision to identify and characterize systematic biases within individual 1000
× 1000 km2 regions.
4.2 Orbit Sampling Time of Day and Latitude Range
As noted above, OCO will fly in a sun synchronous, polar orbit with an ascending 1326 LST
equator crossing time. A series of synthesis inversion calculations, using the set-up of Rayner et
al. [2002] previously described, were performed to ensure that space-based XCO2 data acquired at
this time of day yield the precision needed to characterize regional scale CO2 sources and sinks.
These OSSE’s also allowed us to assess the sensitivity of flux inversions to the range of solar
zenith angles (SZA) sampled by the XCO2 data. Three orbit choices were tested using the full
data set, (i.e. no cloud obscuration):
1. 1100 orbit, with a solar zenith angle cut-off < 75º (399171 data points)
2. 1400 orbit, with a solar zenith angle cut-off < 75º (391796 data points).
3. 1400 orbit, with a solar zenith angle cut-off < 60º (258143 data points)
All times are local solar times (LST) and refer to ascending equatorial crossing. Note that with
the 1-hour time step in CRC-MATCH, 1400 LST seemed the best match to the OCO equatorial
crossing time of 1326 LST. All inversions assumed an OCO XCO2 data precision of 1 ppm.
Figure 12 shows January and July CO2 flux uncertainties, in gC m−2 yr−1, for each of the three
Sunday, September 10, 2023 10:11 PM 29
8384
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
85
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
orbit/SZA cases. The prior uncertainty for all regions was 2000 gC m−2 yr−1. In general, larger
uncertainties are seen in smaller regions because the smaller regions are sampled less frequently
than larger ones. The results for the 1100 and 1400 orbits that sample the globe at SZA < 75o are
very similar. There are large uncertainties at high latitudes in the winter hemisphere where the
SZAs are largest. This is more noticeable in the Northern Hemisphere in January than in the
Southern Hemisphere in July because the average region size is smaller in the northern high
latitudes than the southern high latitudes. As expected, the SZA <60o case gives larger
uncertainties in winter at mid to high latitudes than the SZA < 75o cases. It is noticeable that the
loss of information impacts regions closer to the equator (to 30º) in the SZA <60 o case relative to
the SZA < 75o cases. We would expect a SZA <60o case to produce similar effects on the 1100
orbit.
These simulations verify that space-based measurements from the OCO orbit are sufficient to
meet the mission sampling requirements as well as providing explicit constraints on the range of
SZAs over which measurements must be recorded. The OCO Science Requirements now
specify that the observatory shall be capable of acquiring data at solar zenith angles as large as
75o in glint mode, and at solar zenith angles as large as 85o in nadir mode.
4.3 Diurnal Sampling Bias
In addition to providing XCO2 data with adequate precision to resolve key spatial and temporal
XCO2 gradients, the OCO mission design also minimizes sensitivity to diurnal variations in the
XCO2 data. For example, Haszpra [1999] found that only measurements obtained in the early
afternoon can be considered as regionally representative of the CO2 mixing ratio in the planetary
Sunday, September 10, 2023 10:11 PM 30
8687
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690691
692
693
694
695
696
88
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
boundary layer based on measurements made at two monitoring sites located 220 km apart in the
Hungarian plain. The 1326 LST sun synchronous polar orbit selected in the OCO mission design
minimizes diurnal sampling bias, since the near-surface CO2 concentrations are close to their
diurnally averaged values near this time of day [Olsen and Randerson, 2004]. Additionally, the
largest diurnal variations in CO2 occur near the surface, and the amplitude of these variations
decreases rapidly with height. XCO2 data are therefore inherently much less sensitive to diurnal
variations. CASA/MATCH simulations show that the residual uncertainty after correcting XCO2
retrieved from 1326 LST observations to a 24-hour-averaged value will be < 0.1 ppm, and that
existing models can correct for OCO diurnal sampling bias.
To assess the impact of the 1326 LST sampling bias on the inferred surface CO2 flux inversions,
benchmark surface fluxes were estimated from orbits sampling twice a day at 0600/1800 and
1100/2300 respectively. These orbits are used only to define sampling times for the CO2 fields
for comparison: for example, it would be impossible to measure reflected sunlight at 2300
globally. These fluxes are compared to the flux estimates generated from the 1400 orbit with
SZA < 75º. We find that the differences between the monthly mean source estimates associated
with diurnal sampling bias are usually smaller than the uncertainties on the 1400 orbit source
estimates (e.g. Fig. 12 b). Where larger differences do occur, it is not always possible to attribute
these solely to diurnal biases. For example, sampling biases at high latitudes in the winter
hemisphere due to the lack of sunlight are likely to swamp any diurnal effect there. This suggests
that diurnal sampling biases alone are not a serious problem in estimating CO2 sources and sinks
at monthly intervals.
Sunday, September 10, 2023 10:11 PM 31
8990
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
91
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
4.4 Impact of Clouds on OCO Sampling
We analyzed 1 km resolution MODIS cloud data
to assess the science impact of cloud interference on the OCO sampling strategy. We adopted
the Aqua MODIS products as the most representative of the cloud fields that OCO will
encounter because OCO will fly in formation with the Aqua platform. See Figures 1 and 2 and
Table 1 of Breon et al. [2005] for global distributions of clear sky and almost clear sky
frequency.
To determine the relationship between clear-sky frequency and spatial resolution, we used
MODIS cloud mask results for non-polar daytime surfaces on November 5, 2000. The pixel size
for the MODIS Aqua product is 1 km 1 km. For the present analysis, the MODIS pixels were
aggregated into progressively larger square arrays (2 2 km2, 3 3 km2, 4 4 km2, etc.) with
the array labeled clear if at least 95% of the 1 km pixels were “confident clear” in the MODIS
cloud mask process. Globally averaged results for this analysis are presented in Fig. 13.
The clear-sky frequency decreases rapidly with increasing field of view (FOV) area up to about
36 km2 (6 km 6 km). For FOVs larger than 36 km2, the clear-sky frequency continues to
decrease with increasing area, asymptotically approaching the 10% clear sky fraction commonly
quoted for global averages, but the dependence on FOV area is significantly weaker. Fig. 13
suggests that the clear-sky fraction for the 3 km2 OCO FOV is approximately 24%, a value more
than two times larger than the 10% clear-sky fraction assumed in early OCO mission design
calculations. This analysis thus increases confidence that the OCO small footprint sampling
strategy will provide a sufficient number of clear soundings for accurate XCO2 retrievals. The
Sunday, September 10, 2023 10:11 PM 32
9293
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
736
737
738
739
740
741
742
743
94
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
potential impact of a clear sky bias on the CO2 fluxes inferred from OCO XCO2 data is a question
that requires further investigation.
4.5 Flux Errors for Nadir and Glint Modes
OCO science observations will alternate between Nadir and Glint observing modes on
subsequent 16-day repeat cycles. In Nadir mode, the spacecraft will collect data along the
spacecraft ground track. This mode will provide the highest spatial resolution, and is expected to
yield the most reliable data over continents, in regions occupied by patchy clouds, where spatial
inhomogeneities could introduce systematic errors in the XCO2 product. The primary shortcoming
of this mode is that it is expected to yield lower measurement signal-to-noise ratios (SNR) over
dark ocean surfaces. Glint mode addresses this issue by pointing the instrument boresight at the
point on the surface where sunlight is specularly reflected toward the spacecraft. This mode is
expected to yield measurements with a much higher SNR over the ocean, especially at high
latitudes.
Using the set-up of Rayner et al. [2002], we performed three simulations to compare the
uncertainties in the fluxes returned from observations made in Nadir and Glint modes. The
comparison is not perfect since the Glint calculations were explicitly screened for cloud as the
ground-track was calculated while the Nadir calculations were not. To accommodate these
differences, we normalized the data uncertainty to mimic equal sampling density in Nadir and
Glint modes. The principal remaining difference between the two inversions is the coverage,
which depends primarily on the choice of SZA cut-off. To focus exclusively on differences
associated with the viewing geometry, we chose a SZA < 70º for both inversions. This is
Sunday, September 10, 2023 10:11 PM 33
9596
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758759
760
761
762
763
764
765
766
97
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
somewhat pessimistic choice, since the nominal mission will acquire data at SZA as large as o =
75o in Glint mode and as large as o = 85o in Nadir mode. An additional Nadir mode inversion
was performed in which all soundings over oceans were omitted. This mimics a worst case
scenario, where low albedos preclude reliable XCO2 retrievals from Nadir observations over
ocean.
Flux errors from these three inversion experiments are compared in Fig. 14. The inversions show
little difference in the surface flux uncertainties for the nominal Glint and Nadir cases. This is
not surprising since both modes provide similar coverage and were constrained to provide XCO2
data with the same precision. The reduced spatial coverage degrades high latitude winter
performance relative to the baseline case (Fig. 6a-b). It is also interesting to note that even if
Nadir over ocean are omitted (Fig. 14c), the dense spatial coverage provided over continents by
the space-based XCO2 measurements still offers an advantage over land regions compared to the
existing flask and augmented continuous monitoring networks (Fig. 5).
4.6 Regional Scale XCO2 Representativeness Errors
Accurate surface flux inversions do not require space-based XCO2 data with contiguous spatial
sampling due to atmospheric transport [Chevallier et al., 2006] and representativeness scale
lengths [Gerbig et al., 2003; Lin et al., 2004]. OCO uses 3 km2 footprints and a 10 km cross
track swath to minimize potential biases associated with clouds and other sources of
heterogeneity in the atmosphere and surface. It samples the atmosphere and surface rather than
mapping them. Inferring surface CO2 fluxes from OCO XCO2 data requires careful consideration,
since inversion models typically use grids significantly larger (100 – 1000 km) than the OCO
Sunday, September 10, 2023 10:11 PM 34
9899
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
100
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
cross track swath (10 km). It is important that models aggregate OCO data to accurately
represent spatial and temporal averages at the inversion model resolution. If this is not the case,
the representativeness errors could be substantially increased.
To address the question of short-term mesoscale representativeness errors in surface CO2 fluxes
inferred from OCO XCO2 data, we performed a five-day simulation of surface fluxes and
atmospheric CO2 concentration using the Regional Atmospheric Modeling System (RAMS)
coupled to the Simple Biosphere Model (SiB2) on a nested set of four grids centered on the
WLEF tall tower site in Park Falls Wisconsin for 26 – 30 July, 1997. Overall results and
comparison to the tower observations are reported in Nicholls et al. [2004]. Here we report an
analysis of potential representativeness error of north-south swaths of XCO2 in the central part of
the domain, formed by a 38 × 38 grid with a 1 km × 1 km grid spacing. There are 45 vertical
levels in this simulation, extending to 7.2 km.
Several small lakes in the vicinity of the tower produced anomalous surface fluxes and, more
importantly, anomalous circulations on some afternoons, leading to variations of CO2 of as much
as 6 ppm in the planetary boundary layer. These variations are apparent, although much weaker,
in the column mean. Figure 15 shows the spatial variations in simulated XCO2 for 4 times during a
24-hour period extending over 28-29 July, 1997.
We evaluated possible representativeness errors associated with mesoscale variations by
comparing the mean of 1-km-wide N-S swaths of simulated column mean CO2 with the "true"
domain-averaged column mean mixing ratio over the 38 km × 38 km grid at 1400 LST on each
Sunday, September 10, 2023 10:11 PM 35
101102
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
103
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
of the five days. The range of column mean mixing ratio at 1400 LST over the five days was
0.98 ppm. Spatial autocorrelation of swath means was quite high among swaths within 5 km of
the target swath, so we used 19 degrees of freedom (38 swaths per day times five days divided
by 10 autocorrelated neighboring swaths) in a t-test. Under these conditions, we found that 95%
of the swaths represented had mean mixing ratios within 0.18 ppm of the true domain-averaged
mixing ratio.
We also performed a similar calculation on a regional domain of 600 km × 600 km with 16-km
grid spacing. Substantial spatial variability is imposed on the regional scale by the presence of
the Great Lakes on the east side of the domain. The range of column mean mixing ratio at 1400
LST was larger than on the mesoscale domain, with variations of 3.4 ppm over the five days.
Nevertheless, 95% of the N-S swaths captured the domain average within 0.17 ppm. These
simulations provide confidence that the baseline OCO sampling strategy will deliver precise
space-based XCO2 data even in the presence of representativeness errors associated with realistic
spatial variations in XCO2, since typical representativeness errors are much smaller than 1 ppm.
These results also provide additional confidence in our ability to validate OCO space-based XCO2
retrievals against ground-based solar-viewing FTS spectra obtained at Park Falls [Washenfelder
et al., 2006; Boesch et al., 2006].
5 Conclusions
Precision requirements for space-based XCO2 data have been assessed for the Orbiting Carbon
Observatory mission from the results of observational system simulation experiments and
synthesis inversion models. The XCO2 precision requirements were determined by evaluating the
Sunday, September 10, 2023 10:11 PM 36
104105
813
814
815
816
817
818
819820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
106
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
variability of spatial and temporal gradients in XCO2, the relationship between XCO2 data precision
and inferred surface CO2 flux uncertainties, and the OCO sampling strategy. The OCO
measurement concept was tested using OSSEs and synthesis inversion models to infer regional
scale CO2 sources and sinks from global and regional XCO2 data.
Simulated OCO XCO2 data was ingested into synthesis inversion models to quantify the
relationship between the XCO2 precision and inferred surface-atmosphere CO2 flux uncertainties.
On a global scale, uniform spatial sampling and the sheer number of space-based XCO2 retrievals
will still reduce the uncertainties in inferred surface-atmosphere CO2 fluxes compared to the
fluxes inferred from the GLOBALVIEW-CO2 network even if the space-based XCO2 data had
precisions as poor as 5 ppm on regional scales. XCO2 precisions of 1 – 2 ppm are needed on
regional scales to improve our knowledge of carbon cycle phenomena. Simulated sampling of
CO2 data fields demonstrated the ability of the OCO measurement concept to constrain regional
fluxes of CO2 and quantified the relationship between XCO2 data precision and the ability to
distinguish regional CO2 fluxes from China and India.
The impact of systematic XCO2 biases on CO2 flux uncertainties depends on the spatial and
temporal extent of a bias since CO2 sources and sinks are inferred from regional-scale XCO2
gradients. Source-sink inversion modeling demonstrated that a land/ocean systematic bias as
small as 0.1 ppm could be identified and removed from the XCO2 data product. Biases on spatial
scales smaller than ~104 km2 may be discounted since they will appear the same as random noise.
Constant global scale XCO2 biases do not affect the CO2 flux uncertainties since they introduce no
error into the XCO2 gradients. Persistent geographic biases at the regional to continental scale will
Sunday, September 10, 2023 10:11 PM 37
107108
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
109
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
have the largest impact on the inferred CO2 surface fluxes. Therefore, the OCO validation
program must identify and correct regional to continental scale XCO2 biases.
Acknowledgments. This work was supported by the Orbiting Carbon
Observatory (OCO) project through NASA’s Earth System Science Pathfinder
(ESSP) program. SCO and JTR were supported by a NASA IDS grant (NAG5-
9462) to JTR. We thank R. Frey for assistance with the MODIS cloud data.
Note Added in Proof: Since the completion of the work reported here,
Chevallier et al. [2006] performed inversion experiments which demonstrate
that OCO XCO2 data reduce inferred surface CO2 flux uncertainties even more
than anticipated in the analyses presented above.
Sunday, September 10, 2023 10:11 PM 38
110111
859
860
861
862
863
864
865
866
867
868
869
870
112
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
References
Abdou, W.A., C.J. Bruegge, M.C. Helmlinger, J.E. Conel, S.H. Pilorz, W. Ledeboer,, B.J. Gaitley, K.J. Thome (2002), Vicarious Calibration Experiment in Support of the Multi-angle Imaging SpectroRadiometer, IEEE Trans. Geoscince Remote Sens., 40, 1500 – 1511.
Anderson, B.E., G.L. Gregory, J.E. Collins, G.W. Sachse, T.J. Conway, and G.P. Whiting, Airborne observations of spatial and temporal variability of tropospheric carbon dioxide (1996), J. Geophys. Res., 101 (D1), 1985-1997.
Andres, R.J., G. Marland, I. Fung, E. Matthews (1996), A 1º×1º distribution of carbon dioxide emissions from fossil fuel consumption and cement manufacture, 1950-1990, Global Biogeochem. Cycles, 10, 419-429.
Andrews, A.E., K.A. Boering, B.C. Daube, S.C. Wofsy, E.J. Hintsa, E.M. Weinstock, T.P. Bui (1999), Empirical age spectra for the lower tropical stratosphere from in situ observations of CO2: Implications for stratospheric transport, J. Geophys. Res., 104, 26581-26595.
Andrews, A.E., K.A. Boering, B.C. Daube, S.C. Wofsy, M. Loewenstein, H. Jost, J.R. Podolske, C.R. Webster, R.L. Herman, D.C. Scott, G.J. Flesch, E.J. Moyer, J.W. Elkins, G.S. Dutton, D.F. Hurst, F.L. Moore, E.A. Ray, P.A. Romashkin, S.E. Strahan (2001a), Mean ages of stratospheric air derived from in situ observations of CO2, CH4, and N2O, J. Geophys. Res., 106, 32295-32314.
Andrews, A.E., K.A. Boering, S.C. Wofsy, B.C. Daube, D.B. Jones, S. Alex, M. Loewenstein, J.R. Podolske, S.E. Strahan (2001b), Empirical age spectra for the midlatitude lower stratosphere from in situ observations of CO2: Quantitative evidence for a subtropical "barrier" to horizontal transport, J. Geophys. Res., 106, 10257-10274.
Baker, D. F., et al. (2006a), TransCom 3 inversion intercomparison: Impact of transport model errors on the interannual variability of regional CO2 fluxes, 1988-2003, Global Biogeochem. Cycles, 20, GB1002, doi:10.1029/2004GB002439
Baker, D.F., S.C. Doney, and D.S. Schimel (2006b), Variational data assimilation for atmospheric CO2, Tellus, submitted.
Bakwin, P.S., P.P. Tans, D.F. Hurst, C.L. Zhao (1998), Measurements of carbon dioxide on very tall towers: results of the NOAA/CMDL program, Tellus, 50B, 401-415.
Bakwin, P.S., P.P. Tans, B.B. Stephens, S.C. Wofsy, C. Gerbig, A. Grainger (2003), Strategies for measurement of atmospheric column means of carbon dioxide from aircraft using discrete sampling, J. Geophys. Res., 108(D16), 4514, doi:10.1029/2002JD003306.
Boesch, H., G.C. Toon, B. Sen, R. Washenfelder, P. O. Wennberg, M. Buchwitz, R. de Beek, J. P. Burrows, D. Crisp, M. Christi, B. J. Connor, V. Natraj, Y. L. Yung (2006), Space-based Near-infrared CO2 Retrievals: Testing the OCO Retrieval and Validation Concept Using SCIAMACHY Measurements over Park Falls, Wisconsin, submitted to Journal of Geophysical Research.
Breon, F.M., D.M. O'Brien, J.D. Spinhirne (2005), Scattering layer statistics from space borne GLAS observations, Geophys. Res. Lett., 32, L22802, doi:10.1029/2005GL023825.
Chevallier, F., F.-M. Bréon, P.J. Rayner (2006), The Contribution of the Orbiting Carbon Observatory to the Estimation of CO2 Sources and Sinks: Theoretical Study in a Variational Data Assimilation Framework, submitted to Journal of Geophysical Research.
Sunday, September 10, 2023 10:11 PM 39
113114
871872873874875876877878879880881882883884885886887888889890891892893894895896897898899900901902903904905906907908909910911912913
115
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Cosnefroy, H., M. Leroy, X. Briottet (1996), Selection and Characterization of Saharan and Arabian Desert Sites for the Calibration of Optical Satellite Sensors, Remote Sens. Environ., 58, 101-114.
Cox, C. and W. Munk (1954), Measurement of the roughness of the sea surface from photographs of the Sun s glitter, J. Opt. Soc. Am., 44, 838.
Cox, P.M., R.A. Betts, C.D. Jones, S.A. Spall, and I.J. Totterdell, 2000: Acceleration of global warming due to carbon cycle feedbacks in a coupled climate model, Nature, 408, 184-187.
Cressie, N.A.C. (1991) Statistics for spatial data. Wiley, New York, 900p.Crisp, D., R.M. Atlas, F.M. Breon, L.R. Brown, J.P. Burrows, P. Ciais, B.J. Connor, S.C. Doney,
I.Y. Fung, D.J. Jacob, C.E. Miller, D. O'Brien, S. Pawson, J.T. Randerson, P. Rayner, R.J. Salawitch, S.P. Sander, B. Sen, G.L. Stephens, P.P. Tans, G.C. Toon, P.O. Wennberg, S.C. Wofsy, Y.L. Yung, Z. Kuang, B. Chudasama, G. Sprague, B. Weiss, R. Pollock, D. Kenyon, S. Schroll (2004), The orbiting carbon observatory (OCO) mission, Adv. Space Sci., 34, 700-709.
Crutzen, P.J. (2002), Geology of mankind, Nature, 415, 23.Denning, A.S., M. Holzer, K.R. Gurney, M. Heimann, R.M. Law, P.J. Rayner, I.Y. Fung, S.M.
Fan, S. Taguchi, P. Friedlingstein, Y. Balkanski, J. Taylor, M. Maiss, I. Levin (1999), Three-dimensional transport and concentration of SF6 - A model intercomparison study (TransCom 2), Tellus, 51B, 266-297.
Dilling, L., S.C. Doney, J. Edmonds, K.R. Gurney, R. Harriss, D. Schimel, B. Stephens, G. Stokes (2003), The role of carbon cycle observations and knowledge in carbon management, Ann. Rev. Environ. Resources, 28, 521-558, doi:10.1146/annurev.energy.28.011503.163443.
Dinguirard, M., P. N. Slater (1999), Calibration of Space-Multispectral Imaging Sensors: A Review, Remote Sens. Environ., 68, 194–205.
Dufour, E., F.-M. Breon (2003), Spaceborne estimate of atmospheric CO2 column by use of the differential absorption method: error analysis, Appl. Opt., 42, 3595-3609.
Friedlingstein, P., P.M. Cox, R.A. Betts, L. Bopp, W. von Bloh, V. Brovkin, P. Cadule, S.C. Doney, M. Eby, I. Fung, G. Bala, J. John, C.D. Jones, F. Joos, T. Kato, M. Kawamiya, W. Knorr, K. Lindsay, H.D. Matthews, T. Raddatz, P. Rayner, C. Reick, E. Roeckner, K.-G. Schnitzler, R. Schnur, K. Strassmann, A.J. Weaver, C. Yoshikawa, N. Zeng (2006), Climate-carbon cycle feedback analysis, results from the C4MIP model intercomparison, J. Climate, 19, 3337-3353.
Fung, I., S.C. Doney, K. Lindsay, and J. John, 2005: Evolution of carbon sinks in a changing climate, Proc. Nat. Acad. Sci., 102, 11201-11206, doi:10.1073/pnas.0504949102.
Gerbig, C., J.C. Lin, S.C. Wofsy, B.C. Daube, A.E. Andrews, B.B. Stephens, P.S. Bakwin, and C.A. Grainger (2003), Toward constraining regional-scale fluxes of CO2 with atmospheric observations over a continent: 1. Observed spatial variability from airborne platforms, J. Geophys. Res., 108(D24), 4756, doi:10.1029/2002JD003018.
GLOBALVIEW-CO2, Cooperative Atmospheric Data Integration Project - Carbon Dioxide. CD-ROM, NOAA CMDL, Boulder, Colorado [Also available on Internet via anonymous FTP to ftp.cmdl.noaa.gov, Path: ccg/co2/GLOBALVIEW], 2005.
Gurney, K.R., R.M. Law, A.S. Denning, P.J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler, Y.H. Chen, P. Ciais, S. Fan, I.Y. Fung, M. Gloor, M. Heimann, K. Higuchi, J. John, T. Maki, S. Maksyutov, K. Masarie, P. Peylin, M. Prather, B.C. Pak, J. Randerson, J. Sarmiento, S. Taguchi, T. Takahashi, C.W. Yuen (2002), Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models, Nature, 415, 626-630.
Sunday, September 10, 2023 10:11 PM 40
116117
914915916917918919920921922923924925926927928929930931932933934935936937938939940941942943944945946947948949950951952953954955956957958959
118
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Gurney, K.R., R.M. Law, A.S. Denning, P.J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler, Y.H. Chen, P. Ciais, S.M. Fan, I.Y. Fung, M. Gloor, M. Heimann, K. Higuchi, J. John, E. Kowalczyk, T. Maki, S. Maksyutov, P. Peylin, M. Prather, B.C. Pak, J. Sarmiento, S. Taguchi, T. Takahashi, C.W. Yuen (2003), TransCom 3 CO2 inversion intercomparison: 1. Annual mean control results and sensitivity to transport and prior flux information, Tellus, 55B, 555-579.
Gurney, K.R., R.M. Law, A.S. Denning, P.J. Rayner, B.C. Pak, D. Baker, P. Bousquet, L. Bruhwiler, Y.H. Chen, P. Ciais, I.Y. Fung, M. Heimann, J. John, T. Maki, S. Maksyutov, P. Peylin, M. Prather, S. Taguchi (2004), TransCom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks, Global Biogeochem. Cycles, 18, GB1010, doi:10.1029/2003GB002111.
Gurney, K.R., Y.H. Chen, T. Maki, S.R. Kawa, A. Andrews, Z.X. Zhu (2005), Sensitivity of atmospheric CO2 inversions to seasonal and interannual variations in fossil fuel emissions, J. Geophys. Res., 110, D10308, doi:10.1029/2004JD005373.
Haszpra, L. (1999), On the representativeness of carbon dioxide measurements, J. Geophys. Res., 104, 26953-26960.
Haszpra, L., Z. Barcza, K.J. Davis, K. Tarczay (2005), Long-term tall tower carbon dioxide flux monitoring over an area of mixed vegetation, Agri. Forest Meteor., 132, 58-77.
Houweling, S., F.M. Breon, I. Aben, C. Rodenbeck, M. Gloor, M. Heimann, P. Ciais (2004), Inverse modeling of CO2 sources and sinks using satellite data: a synthetic inter-comparison of measurement techniques and their performance as a function of space and time, Atm. Chem. Phys., 4, 523-538.
IPCC, 2001: Climate Change 2001: Synthesis Report. A Contribution of Working Groups I, II, and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change, 398 pp., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2001.
Kleidman, R.G., Y.J. Kaufman, B.C. Gao, L.A. Remer, V.G. Brackett, R.A. Ferrare, E.V. Browell, S. Ismail (2000), Remote sensing of total precipitable water vapor in the near-IR over ocean glint, Geophys. Res. Lett., 27, 2657-2660.
Kuang, Z., J. Margolis, G. Toon, D. Crisp, Y. Yung (2002), Spaceborne measurements of atmospheric CO2 by high-resolution NIR spectrometry of reflected sunlight: An introductory study, Geophys. Res. Lett., 29, doi:10.1029/2001GL014298.
Law, R.M., Y.H. Chen, K.R. Gurney (2003), TransCom 3 CO2 inversion intercomparison: 2. Sensitivity of annual mean results to data choices, Tellus, 55B, 580-595.
Lin, J.C., C. Gerbig, B.C. Daube, S.C. Wofsy, A.E. Andrews, S.A. Vay, B.E. Anderson (2004), An empirical analysis of the spatial variability of atmospheric CO2: Implications for inverse analyses and space-borne sensors, Geophys. Res. Lett., 31, L23104, doi:10.1029/2004GL020957.
Machida, T., K. Kita, Y. Kondo, D. Blake, S. Kawakami, G. Inoue, and T. Ogawa, Vertical and meridional distributions of the atmospheric CO2 mixing ratio between northern midlatitudes and southern subtropics, J. Geophys. Res., 107, 8401, doi:10.1029/2001JD000910, 2002. [printed 108(D3), 2003.]
Mao, J.P., S.R. Kawa (2004), Sensitivity studies for space-based measurement of atmospheric total column carbon dioxide by reflected sunlight, Appl. Opt., 43, 914-927.
Matsueda, H., H.Y. Inoue, and M. Ishii (2002), Aircraft observation of carbon dioxide at 8-13 km altitude over the western Pacific from 1993 to 1999, Tellus, 54B, 1-21.
Sunday, September 10, 2023 10:11 PM 41
119120
960961962963964965966967968969970971972973974975976977978979980981982983984985986987988989990991992993994995996997998999
100010011002100310041005
121
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Michalak, A.M., L. Bruhwiler, P.P. Tans. (2004), A geostatistical approach to surface flux estimation of atmospheric trace gases, J. Geophys. Res., 109, D14109, doi:10.1029/2003JD004422.
Nicholls, M.E., A.S. Denning, L. Prihodko, P.L. Vidale, I. Baker, K. Davis, P. Bakwin (2004), A multiple-scale simulation of variations in atmospheric carbon dioxide using a coupled biosphere-atmospheric model, J. Geophys. Res., 109, D18117, doi:10.1029/2003JD004482.
O’Brien, D. M., and P. J. Rayner (2002), Global observations of the carbon budget, 2. CO2 column from differential absorption of reflected sunlight in the 1.61 um band of CO2, J. Geophys. Res., 107(D18), 4354, doi:10.1029/2001JD000617.
Olsen, S. C., and J. T. Randerson (2004), Differences between surface and column atmospheric CO2 and implications for carbon cycle research, J. Geophys. Res., 109, D02301, doi:10.1029/2003JD003968.
Palmer, P. I., D. J. Jacob, D. B. A. Jones, C. L. Heald, R. M. Yantosca, J. A. Logan, G. W. Sachse, and D. G. Streets (2003), Inverting for emissions of carbon monoxide from Asia using aircraft observations over the western Pacific, J. Geophys. Res., 108(D21), 8828, doi:10.1029/2003JD003397.
Palmer, P. I., P. Suntharalingam, D. B. A. Jones, D. J. Jacob, D. G. Streets, Q. Fu, S. A. Vay, and G. W. Sachse (2006), Using CO2:CO correlations to improve inverse analyses of carbon fluxes, J. Geophys. Res., 111, D12318, doi:10.1029/2005JD006697.
Ramonet, M., P. Ciais, I. Nepomniachii, K. Sidorov, R.E.M. Neubert, U. Langendorfer, D. Picard, V. Kazan, S. Biraud, M. Gusti, O. Kolle, E.D. Schulze, and J. Lloyd (2002), Three years of aircraft-based trace gas measurements over the Fyodorovskoye southern taiga forest, 300 km north-west of Moscow, Tellus, 54B, 713-734.
Randerson, J.T., M.V. Thompson, T.J. Conway, I.Y. Fung, C.B. Field (1997), The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide, Global Biogeochem. Cycles, 11, 535-560.
Rasch, P.J., X.X. Tie, B.A. Boville, D.L. Williamson (1994), A 3-Dimensional Transport Model for the Middle Atmosphere, J. Geophys. Res., 99, 999-1017.
Rayner, P. J. and D. M. O'Brien (2001), The utility of remotely sensed CO2 concentration data in surface source inversions, Geophys. Res. Lett., 28, 175-178.
Rayner, P. J., R. M. Law, D. M. O’Brien, T. M. Butler, and A. C. Dilley (2002), Global observations of the carbon budget, 3, Initial assessment of the impact of satellite orbit, scan geometry, and cloud on measuring CO2 from space, J. Geophys. Res., 107(D21), 4557, doi:10.1029/2001JD000618.
Sawa, Y., H. Matsueda, Y. Makino, H.Y. Inoue, S. Murayama, M. Hirota, Y. Tsutsumi, Y. Zaizen, M. Ikegami, K. Okada (2004), Aircraft observation of CO2, CO, O3 and H2 over the North Pacific during the PACE-7 campaign, Tellus, 56B, 2-20.
Streets, D. G., et al. (2003), An inventory of gaseous and primary aerosol emissions in Asia in the year 2000, J. Geophys. Res., 108(D21), 8809, doi:10.1029/2002JD003093.
Suntharalingam, P., D. J. Jacob, P. I. Palmer, J. A. Logan, R. M. Yantosca, Y. Xiao, M. J. Evans, D. G. Streets, S. L. Vay, and G. W. Sachse (2004), Improved quantification of Chinese carbon fluxes using CO2/CO correlations in Asian outflow, J. Geophys. Res., 109, D18S18, doi:10.1029/2003JD004362.
Takahashi, T., S.C. Sutherland, C. Sweeney, A. Poisson, N. Metzl, B. Tilbrook, N. Bates, R. Wanninkhof, R.A. Feely, C. Sabine, J. Olafsson, Y. Nojiri (2002), Global sea-air CO2 flux
Sunday, September 10, 2023 10:11 PM 42
122123
1006100710081009101010111012101310141015101610171018101910201021102210231024102510261027102810291030103110321033103410351036103710381039104010411042104310441045104610471048104910501051
124
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
based on climatological surface ocean pCO2, and seasonal biological and temperature effects, Deep-Sea Research Part II-Topical Studies in Oceanography, 49 (9-10), 1601-1622.
Tans, P.P., I.Y. Fung, T. Takahashi (1990), Observational Constraints on the Global Atmospheric CO2 Budget, Science, 247, 1431-1438.
Vay, S.A., J.H. Woo, B.E. Anderson, K.L. Thornhill, D.R. Blake, D.J. Westberg, C.M. Kiley, M.A. Avery, G.W. Sachse, D.G. Streets, Y. Tsutsumi, S.R. Nolf (2003), Influence of regional-scale anthropogenic emissions on CO2 distributions over the western North Pacific, J. Geophys. Res., 108(D20), 8801, doi:10.1029/2002JD003094.
Wackernagel, H., Multivariate Geostatistics, 387 pp., Springer, Berlin, 2003.Washenfelder, R.A., G.C. Toon, J-F. Blavier, Z. Yang, N.T. Allen, P.O. Wennberg, S.A. Vay,
D.M. Matross, B.C. Daube (2006), Carbon dioxide column abundances at the Wisconsin Tall Tower site, submitted to Journal of Geophysical Research.
Sunday, September 10, 2023 10:11 PM 43
125126
1052105310541055105610571058105910601061106210631064
127
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Figure Captions
Figure 1. Modeled column-averaged CO2 dry air mole fraction (XCO2) simulated for the year 2000 using the CASA/MATCH model [Olsen and Randerson, 2004]. Values for 1300 LST on January 15 and July 15 are plotted relative to the annual average XCO2 south of 60oS.
Figure 2. Global experimental and fitted theoretical variograms for CASA/MATCH modeled column-averaged CO2 dry air mole fraction (XCO2) presented in Figure 1 (1300 LST for January 15 and July 15, 2000).
Figure 3. Locally-estimated correlation lengths for CASA/MATCH modeled column-averaged CO2 dry air mole fraction (XCO2) presented in Figure 1 (1300 LST for January 15 and July 15, 2000). In general, more XCO2 retrievals will be required to characterize regions with shorter (red) correlation lengths.
Figure 4. Locally-estimated variance for CASA/MATCH modeled column-averaged CO2 dry air mole fraction (XCO2) presented in Figure 1 (1300 LST for January 15 and July 15, 2000). In general, more XCO2 retrievals will be required to characterize regions with larger (red) variances.
Figure 5. (a–b) Surface CO2 flux uncertainties (gC m2 yr1) for January (a) and July (b) from the baseline synthesis inversion case where the simulated CO2 data are obtained from a surface network similar to the current network, which provides highly precise CO2 pseudodata from 72 flask sites at weekly intervals. Small uncertainties are shaded in blue, large uncertainties in red/pink. Note that the scale is non-linear. The flux uncertainties over the continents have not improved significantly over the a priori uncertainty of 2000 gC m2 yr1. (c–d) CO2 flux uncertainties for a simulated surface network that uses continuous surface pseudodata from 25 sites plus weekly flask pseudodata from the remaining 47 sites. See text for details.
Figure 6. Surface CO2 flux uncertainties (gC m2 yr1) for January and July from simulations that used satellite pseudodata of XCO2 with precision of 1 ppm (a-b) and 5 ppm (c-d). Compare to Fig. 5.
Figure 7. Surface CO2 concentration annual mean differences (ppm) associated with a +0.1 ppm bias in the XCO2 observations acquired over land. The locations of the surface CO2 monitoring network stations are shown as black circles.
Figure 8. Surface CO2 flux errors (gC m2 yr1) inferred from an inversion of XCO2 data biased by +0.1 ppm over land that have been corrected using an inversion of the the XCO2 differences given in Fig.7. Most regions show differences smaller than 10 gC m−2 yr−1 and the differences are no longer spatially coherent.
Figure 9. Asian geographical regions used in the inversion analysis. CO2 surface fluxes from each region are given in Table 2.
Sunday, September 10, 2023 10:11 PM 44
128129
10651066106710681069107010711072107310741075107610771078107910801081108210831084108510861087108810891090109110921093109410951096109710981099110011011102110311041105110611071108
130
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Figure 10. (a) Modeled XCO2 values for March 2001. (b) Comparison of a posteriori flux estimates (calculated with an observation error of 0.3%) with true and a priori fluxes. Red bars: a posteriori fluxes, Blue bars: a priori fluxes, Black bars: true fluxes. Error bars show an uncertainty of 1.
Figure 11. A posteriori uncertainty (relative to “true” flux estimates) as a function of observation error. Red solid line denotes a posteriori estimates starting from an a priori uncertainty of 50%, black dotted line corresponds to the case with a priori uncertainty of 100%, and blue dashed line is for an a priori error of 150%.
Figure 12. Flux uncertainties (gC m2 yr1) for January and July are shown for orbits with sampling times of 1100 and 1400 LST for cases with SZA < 75 (a, b) and with a sampling time of 1400 LST for SZA < 60 (c). Small uncertainties are shaded in blue, large uncertainties in red/pink. Note that the scale is non-linear. Compare to Fig. 6a-b.
Figure 13. Global clear-sky frequency vs. spatial resolution computed using the MODIS 1 km cloud product
Figure 14. Monthly CO2 flux uncertainty (gC m2 yr1) for January (left) and July (right) for (a) Glint mode SZA < 70º, (b) Nadir mode SZA < 70º and (c) Nadir mode SZA < 70º excluding ocean measurements. All observations at 1400 LST. Compare for Fig. 6a-b.
Figure 15. Spatial variations of simulated column mean CO2 mixing ratios (ppm) for 4 different times during July 28, 1997 on the 1-km grid centered at the WLEF tower.
Sunday, September 10, 2023 10:11 PM 45
131132
1109111011111112111311141115111611171118111911201121112211231124112511261127112811291130113111321133
133
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2
Tables
Table 1. Global XCO2 variability at 1:00 PM local time for a representative day in each month of the year 2000 from CASA/MATCH model runs
Month
Correlation Length
(3L) [km]
Variance (22) [ppm2]
January 2900 0.89February 5100 1.08March 5400 1.16April 4900 1.45May 5500 1.06June 4300 1.24July 6100 4.16August 8300 3.71September 7500 2.06October 2800 1.08November 700 0.98December 2700 0.67Annual mean 4700 1.63
Table 2. GEOS-CHEM CO2 fluxes for March-April, 2001.
Region Symbol
C Fluxa
(mg C m-2 d-1)
Areab
(1010 m2)
China CHINA 596 1421
Japan JAPAN 1452 92
Korea KOREA 3993 29
India INDIA 744 336
Southeast Asia SEASIA 533 456
Rest of the Worldc ROTW 32 48389aValues are net fluxes and include contributions from fossil fuel and biofuel combustion, biomass burning, and exchange with the biosphere. The GEOS-CHEM model fluxes are taken as the “true” fluxes for purpose of the OSSEbLand area for flux regions defined in Figure 9cIncludes the CO2 flux associated with the atmosphere-ocean exchange
Sunday, September 10, 2023 10:11 PM 46
134135
1134
11351136
11371138
1139
114011411142
1143
1144
136
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 47
Figure 1. Modeled column-averaged CO2 dry air mole fraction (XCO2) simulated for the year 2000 using the CASA/MATCH model [Olsen and Randerson, 2004]. Values for 1300 LST on January 15 and July 15 are plotted relative to the annual average XCO2 south of 60oS.
Figure 2. Global experimental and fitted theoretical variograms for the modeled XCO2 presented in Figure 1 (1300 LST for January 15 and July 15, 2000).
Sunday, September 10, 2023 10:11 PM 47
137
11451146
11471148114911501151115211531154
115511561157115811591160
138
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 48
Figure 3. Locally-estimated correlation lengths for CASA/MATCH modeled column-averaged CO2 dry air mole fraction (XCO2) presented in Figure 1 (1300 LST for January 15 and July 15, 2000). In general, more XCO2 retrievals will be required to characterize regions with shorter (red) correlation lengths.
Figure 4. Locally-estimated variance for CASA/MATCH modeled column-averaged CO2 dry air mole fraction (XCO2) presented in Figure 1 (1300 LST for January 15 and July 15, 2000). In general, more XCO2 retrievals will be required to characterize regions with larger (red) variances.
Sunday, September 10, 2023 10:11 PM 48
139
1161
11621163116411651166116711681169117011711172
117311741175117611771178117911801181
140
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 49
Figure 5. (a–b) Surface CO2 flux uncertainties (gC m2 yr1) for January (a) and July (b) from the baseline synthesis inversion case where the simulated CO2 data are obtained from a surface network similar to the current network, which provides highly precise CO2 pseudodata from 72 flask sites at weekly intervals. Small uncertainties are shaded in blue, large uncertainties in red/pink. Note that the scale is non-linear. The flux uncertainties over the continents have not improved significantly over the a priori uncertainty of 2000 gC m2 yr1. (c–d) CO2 flux uncertainties for a simulated surface network that uses continuous surface pseudodata from 25 sites plus weekly flask pseudodata from the remaining 47 sites. See text for details.
Sunday, September 10, 2023 10:11 PM 49
141
11821183
118411851186118711881189119011911192119311941195
142
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 50
Figure 6. Surface CO2 flux uncertainties (gC m2 yr1) for January and July from simulations that used satellite pseudodata of XCO2 with precision of 1 ppm (a-b) and 5 ppm (c-d). Compare to Fig. 5.
Sunday, September 10, 2023 10:11 PM 50
143
1196
1197119811991200
144
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 51
Figure 7. Surface CO2 concentration annual mean differences (ppm) associated with a +0.1 ppm bias in the XCO2 observations acquired over land. The locations of the surface CO2 monitoring network stations are shown as black circles.
Sunday, September 10, 2023 10:11 PM 51
145
12011202
120312041205120612071208
146
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 52
Figure 8. Surface CO2 flux errors (gC m2 yr1) inferred from an inversion of XCO2 data biased by +0.1 ppm over land that have been corrected using an inversion of the the XCO2 differences given in Fig.7. Most regions show differences smaller than 10 gC m−2 yr−1 and the differences are no longer spatially coherent.
Sunday, September 10, 2023 10:11 PM 52
147
12091210
12111212121312141215121612171218121912201221122212231224
148
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 53
Figure 9. Asian geographical regions used in the inversion analysis. CO2 surface fluxes from each region are given in Table 2.
Sunday, September 10, 2023 10:11 PM 53
149
12251226
1227122812291230123112321233123412351236
150
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 54
Figure 10. (a) Modeled XCO2 values for March 2001. (b) Comparison of a posteriori flux estimates (calculated with an observation error of 0.3%) with true and a priori fluxes. Red bars: a posteriori fluxes, Blue bars: a priori fluxes, Black bars: true fluxes. Error bars show an uncertainty of 1.
Sunday, September 10, 2023 10:11 PM 54
151
1237123812391240
12411242124312441245124612471248
152
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 55
Figure 11. A posteriori uncertainty (relative to “true” flux estimates) as a function of observation error. Red solid line denotes a posteriori estimates starting from an a priori uncertainty of 50%, black dotted line corresponds to the case with a priori uncertainty of 100%, and blue dashed line is for an a priori error of 150%.
Sunday, September 10, 2023 10:11 PM 55
153
1249
12501251125212531254125512561257
154
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 56
Figure 12. Flux uncertainties (gC m2 yr1) for January and July are shown for orbits with sampling times of 1100 and 1400 LST for cases with SZA < 75 (a, b) and with a sampling time of 1400 LST for SZA < 60 (c). Small uncertainties are shaded in blue, large uncertainties in red/pink. Note that the scale is non-linear. Compare to Fig. 6a-b.
Sunday, September 10, 2023 10:11 PM 56
155
1258125912601261
126212631264126512661267126812691270
156
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 57
Aqua MODIS Clear Sky Fraction: 5 Nov 2000
Pixel Area/km2
2 4 16 36 64 100 144 256 400
Cle
ar S
ky F
ract
ion/
per c
ent
10
15
20
25
30
Figure 13 Global clear-sky frequency vs. spatial resolution computed using the MODIS 1 km cloud product
Sunday, September 10, 2023 10:11 PM 57
157
12711272
12731274127512761277
158
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 58
Figure 14. Monthly CO2 flux uncertainty (gC m2 yr1) for January (left) and July (right) for (a) Glint mode SZA < 70º, (b) Nadir mode SZA < 70º and (c) Nadir mode SZA < 70º excluding ocean measurements. All observations at 1400 LST. Compare for Fig. 6a-b.
Sunday, September 10, 2023 10:11 PM 58
159
127812791280128112821283
160
MILLER ET AL.: PRECISION REQUIREMENTS FOR SPACE-BASED XCO2 DATA 59
Figure 15. Spatial variations of simulated column mean CO2 mixing ratios (ppm) for 4 different times during July 28, 1997 on the 1-km grid centered at the WLEF tower.
Sunday, September 10, 2023 10:11 PM 59
161
12841285128612871288128912901291
162